The “Diabetes in Primary Care – Improving Classification” (DePICtion) study: a feasibility study to determine the utility and acceptability of implementing diabetes classification tools in primary care
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Introduction
There are several subtypes of diabetes, including type1, type 2, and a genetic form called MODY. Classification can be challenging, particularly in young people, with up to 15% of people misclassified with the wrong subtype, resulting in inappropriate treatment. We assessed the feasibility of embedding a decision support tool (the DePICtion tool) in GP records to flag patients at risk of misclassification for further investigation and biomarker testing.
Research Design & Methods
The DePICtion tool was run in 11 UK GP practices. The tool generated a list of patients at high probability of being misclassified for review by practice staff. After review, practices returned a follow-up data form, summarising the actions taken for these patients.
Results
Overall, 7% (237/3501) of patients were flagged as potentially misclassified. Of 203 with follow-up data, 28 (14%) records had coding errors and 2 patients had died, leaving 173 (85%) suitable for biomarker testing to confirm the diagnosis. Around half (88/173) of these patients received new biomarker testing or had previous testing on the system (73 complete, 15 partial or unknown results). 17% (30/173) of patients declined/did not respond. For the remaining 55, the practice reason for not testing did not rule out an alternative diagnosis in the majority of cases (n=44). Among the 73 patients with definitive results, nearly 1 in 5 (18%; 13) changed diagnosis (2 diagnosed with MODY, 6 from type 1 to type 2, 5 from type 2 to type 1).
Conclusions
The DePICtion tool identified people with diabetes that had been misclassified. However, many people flagged as potentially misclassified did not have classification biomarker testing. Better data recording and greater engagement with secondary care could improve uptake of testing to identify more misclassified patients and optimise their diabetes care.
Key messages
What is already known on this topic
Classification of diabetes subtypes can be challenging, and misclassification is common in clinical practice. Classification algorithms have been developed, but their utility in primary care is not known.
What this study adds
We carried out a feasibility study to determine the utility and acceptability of running the diabetes classification algorithms as automated decision support tools in 11 GP practices. We found that the algorithms did identify patients at high risk of misclassification, but that lack of specialist input and GP time were barriers to wider uptake.
How this study might affect research, practice or policy
In line with the NHS 10 year plan, there is a strong drive to move from analogue to digital, but implementation of statistical models into clinical practice needs robust studies to ensure their translation is useful in real-world care. This study provided key data to inform future steps towards large-scale roll out of this approach to ensure misclassified patients are appropriately identified and they can obtain the right treatment and care for their diabetes.
